Data Science for Society at WomenCourage 2019

Graziella Lonardi Buontempo room - MAXXI (National Museum of XXI Century Arts), Rome, Italy
Data Science for Society at WomenCourage 2019

The big data arising from the digital breadcrumbs of human activities promise to let us scrutinize the ground truth of individual and collective behaviour at an unprecedented detail and scale. Big data, combined with social data mining, i.e., adequate means for accessing big data and extracting useful knowledge from them provide a chance to understand the complexity of our contemporary, globally-interconnected society: e.g., disentangling urban sustainability and resilience, societal well-being and its multiple facets, the unequal distribution of resources and opportunities, the "ecological" problems of our information system, such as polarization and misinformation, the dynamics and economic drivers behind human migration. This workshop will focus on examples of social mining and big data research answering challenging questions in different domains that have been developed within the project SoBigData.

On line debates: i) is there any consequence in opinion formation and diffusion due to the algorithm bias present in social media platforms? ; ii) using Twitter as a proxy of our society, to monitor opinions about politicians, looking at abuse broken down by parties and gender.

Migration: discussions about the refugee crisis and the United Kingdom European Union membership referendum. These complex and contended topics can be analyzed monitoring online social networks like Twitter. Discussion about the possibility to infer immigrants' rate by using Twitter data and by exploiting Sentiment Analysis techniques.

City of Citizens: how to invest in car sharing, autonomous drive and electric mobility, as starting point for enabling smart city solutions.

On top of this, a fundamental point that often is forgotten regards ethics and its implication. In particular, we want to describe some of challenging methodologies and solutions to privacy analysis and explainability of algorithms.